50 lines
1.3 KiB
50 lines
1.3 KiB
#!/usr/bin/env python
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from paddle.trainer_config_helpers import *
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height = 32
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width = 32
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num_class = 10
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batch_size = get_config_arg('batch_size', int, 128)
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args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
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define_py_data_sources2(
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"train.list", None, module="provider", obj="process", args=args)
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settings(
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batch_size=batch_size,
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learning_rate=0.01 / batch_size,
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learning_method=MomentumOptimizer(0.9),
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regularization=L2Regularization(0.0005 * batch_size))
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# conv1
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net = data_layer('data', size=height * width * 3)
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net = img_conv_layer(
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input=net,
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filter_size=5,
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num_channels=3,
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num_filters=32,
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stride=1,
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padding=2)
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net = img_pool_layer(input=net, pool_size=3, stride=2, padding=1)
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# conv2
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net = img_conv_layer(
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input=net, filter_size=5, num_filters=32, stride=1, padding=2)
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net = img_pool_layer(
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input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
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# conv3
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net = img_conv_layer(
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input=net, filter_size=3, num_filters=64, stride=1, padding=1)
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net = img_pool_layer(
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input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
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net = fc_layer(input=net, size=64, act=ReluActivation())
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net = fc_layer(input=net, size=10, act=SoftmaxActivation())
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lab = data_layer('label', num_class)
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loss = classification_cost(input=net, label=lab)
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outputs(loss)
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